Merge pull request #2 from pni-lab/manuscript_submission

corrected for submission
pni-lab · May 13, 2024 · 8f01903 · 8f01903
2 parents b40650e + ce6c013
commit 8f01903
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diff --git a/.gitignore b/.gitignore
@@ -1,4 +1,5 @@
 .idea/
 manuscript/_build/
 manuscript/exports/
-**/.DS_Store
+**/.DS_Store
+**/__pycache__
diff --git a/adaptivesplit/__init__.py b/adaptivesplit/__init__.py
@@ -1,6 +0,0 @@
-import adaptivesplit.base.learning_curve
-LearningCurve = adaptivesplit.base.learning_curve.LearningCurve
-
-from ._version import get_versions
-__version__ = get_versions()['version']
-del get_versions

diff --git a/manuscript/01-paper.md b/manuscript/01-paper.md
@@ -277,8 +277,13 @@ The proposed adaptive splitting design can advance the development of predictive
 
 In conclusion, registered models provide a simple approach to guarantee the independence of model discovery and external validation and for the development and initial evaluation of registered models with unknown power, the introduced adaptive splitting procedure provides a robust and flexible approach to determine the optimal ratio of data to be used for model discovery and external validation. Together, registered models and the adaptive splitting procedure, address several common issues in the field, including overfitting, cross-validation failure, and boost the reliability and reproducibility.
 
+##### **Acknowledgements**
+
+The work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 422744262 - TRR 289 (Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 422744262 – TRR 289).
+
+
 [^adaptive-split]: https://github.com/pni-lab/adaptivesplit
 [^ixi]: https://brain-development.org/ixi-dataset/
 [^nilearn]: http://nilearn.github.io/
 [^scikit-learn]: https://scikit-learn.org/stable/
-[^abide-data]: https://osf.io/hc4md
+[^abide-data]: https://osf.io/hc4md
diff --git a/manuscript/03-package.ipynb b/manuscript/03-package.ipynb
diff --git a/manuscript/main.zip.1 b/manuscript/main.zip.1
diff --git a/setup.py b/setup.py
@@ -7,15 +7,15 @@
 # Setting up
 setup(
     name="adaptivesplit",
-    version=VERSION,
+    version="0.0.1",
     author="PNI Lab (Predictive NeuroImaging Laboratory)",
     author_email="<giuseppe.gallitto@uk-essen.de>",
     description=DESCRIPTION,
     long_description_content_type="text/markdown",
     long_description=LONG_DESCRIPTION,
     packages=find_packages(),
     install_requires=["joblib==1.2.0", "matplotlib==3.6.0", "numpy==1.24.2", "pandas==1.5.1", "pygam==0.9.0",
-                      "regressors==0.0.3", "scikit_learn==1.2.1", "scipy==1.11.3", "tqdm==4.64.1"],
+                      "regressors==0.0.3", "scikit_learn==1.2.1", "scipy==1.11.3", "tqdm==4.64.1", "setuptools==57.5.0"],
     keywords=['python', 'machine learning', 'neuroimaging', 'data splitting'],
     classifiers=[]
 )